Abstract: According to the complementary on convergence of ant colony algorithm and genetic algorithm, a hybrid ant colony genetic algorithm based on the change rate of objective function is put forward. The basic idea of new algorithm is that the solutions of ant colony algorithm are given as the initial population of genetic algorithm, and the ant colony algorithm and genetic algorithm are dynamically called according to the change rate of objective function. When population evolutionary is close to stagnation, ant colony algorithm is called. This approach can dynamically control the call timing of ant colony algorithm and genetic algorithm. Together with the pheromone update methods, the convergence of hybrid algorithm is improved. The new algorithm is used to solve the Job shop scheduling benchmarks problem, and the simulation results show that the global and local convergence performance of new algorithm has been significantly improved compared with basic hybrid ant colony genetic algorithm.

Key words: Ant Colony Optimization（ACO）, Genetic Algorithm（GA）, change rate of objective function, Job shop scheduling benchmarks problem

摘要： 根据蚁群算法和遗传算法收敛性互补的特点，提出了一种基于目标函数变化率的混合蚁群遗传算法。该算法的基本思想是：用蚁群算法的解作为遗传算法的初始种群，根据目标函数的变化率交叉地调用蚁群算法和遗传算法。每当种群进化接近停滞时，调用蚁群算法。这种方法可动态地控制蚁群算法和遗传算法的调用时机，再配合相应的信息素更新方法，以提高算法的收敛性。将新算法用于车间调度基准测试问题，仿真结果表明，与常规混合蚁群遗传算法相比，新算法的全局收敛性和局部收敛性有了明显的提高。

关键词: 蚁群算法, 遗传算法, 目标函数变化率, 车间调度基准问题